Overview

Dataset statistics

Number of variables14
Number of observations113
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.3 KiB
Average record size in memory156.9 B

Variable types

Numeric12
Categorical2

Alerts

lenght_of_stay is highly overall correlated with infection_risk and 4 other fieldsHigh correlation
infection_risk is highly overall correlated with lenght_of_stay and 1 other fieldsHigh correlation
routine_culturing_ratio is highly overall correlated with infection_riskHigh correlation
num_beds is highly overall correlated with lenght_of_stay and 7 other fieldsHigh correlation
avg_census is highly overall correlated with lenght_of_stay and 7 other fieldsHigh correlation
num_nurses is highly overall correlated with num_beds and 6 other fieldsHigh correlation
avelbl_services is highly overall correlated with num_beds and 6 other fieldsHigh correlation
ln_num_beds is highly overall correlated with lenght_of_stay and 6 other fieldsHigh correlation
ln_avg_census is highly overall correlated with lenght_of_stay and 7 other fieldsHigh correlation
ln_num_nurses is highly overall correlated with num_beds and 6 other fieldsHigh correlation
med_school_affil is highly overall correlated with num_beds and 5 other fieldsHigh correlation

Reproduction

Analysis started2023-11-10 17:45:46.433508
Analysis finished2023-11-10 17:46:11.403709
Duration24.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

lenght_of_stay
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6483186
Minimum6.7
Maximum19.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:11.789260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile7.29
Q18.34
median9.42
Q310.47
95-th percentile12.034
Maximum19.56
Range12.86
Interquartile range (IQR)2.13

Descriptive statistics

Standard deviation1.911456
Coefficient of variation (CV)0.19811286
Kurtosis8.0774899
Mean9.6483186
Median Absolute Deviation (MAD)1.08
Skewness2.0689174
Sum1090.26
Variance3.6536641
MonotonicityNot monotonic
2023-11-10T17:46:11.995917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.14 3
 
2.7%
9.89 2
 
1.8%
9.84 2
 
1.8%
8.28 2
 
1.8%
11.2 2
 
1.8%
9.76 2
 
1.8%
11.18 2
 
1.8%
8.88 2
 
1.8%
11.41 2
 
1.8%
8.45 1
 
0.9%
Other values (93) 93
82.3%
ValueCountFrequency (%)
6.7 1
 
0.9%
7.08 1
 
0.9%
7.13 1
 
0.9%
7.14 3
2.7%
7.39 1
 
0.9%
7.53 1
 
0.9%
7.58 1
 
0.9%
7.63 1
 
0.9%
7.65 1
 
0.9%
7.67 1
 
0.9%
ValueCountFrequency (%)
19.56 1
0.9%
17.94 1
0.9%
13.95 1
0.9%
13.59 1
0.9%
12.78 1
0.9%
12.07 1
0.9%
12.01 1
0.9%
11.8 1
0.9%
11.77 1
0.9%
11.65 1
0.9%

age
Real number (ℝ)

Distinct79
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.231858
Minimum38.8
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:12.226471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.8
5-th percentile45.38
Q150.9
median53.2
Q356.2
95-th percentile60.3
Maximum65.9
Range27.1
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation4.4616074
Coefficient of variation (CV)0.083814608
Kurtosis1.0663988
Mean53.231858
Median Absolute Deviation (MAD)2.6
Skewness-0.10398229
Sum6015.2
Variance19.90594
MonotonicityNot monotonic
2023-11-10T17:46:12.471380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.2 4
 
3.5%
56.9 4
 
3.5%
52.8 4
 
3.5%
51.7 3
 
2.7%
52.1 3
 
2.7%
53.8 3
 
2.7%
49.5 3
 
2.7%
50.6 2
 
1.8%
51.5 2
 
1.8%
55.8 2
 
1.8%
Other values (69) 83
73.5%
ValueCountFrequency (%)
38.8 1
0.9%
42 1
0.9%
43.7 1
0.9%
44.2 1
0.9%
45 1
0.9%
45.2 1
0.9%
45.5 1
0.9%
45.7 1
0.9%
47.1 1
0.9%
47.2 1
0.9%
ValueCountFrequency (%)
65.9 1
0.9%
64.1 1
0.9%
63.9 1
0.9%
62.2 1
0.9%
61.1 1
0.9%
60.9 1
0.9%
59.9 1
0.9%
59.6 1
0.9%
59.5 1
0.9%
59 1
0.9%

infection_risk
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3548673
Minimum1.3
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:12.691135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.92
Q13.7
median4.4
Q35.2
95-th percentile6.34
Maximum7.8
Range6.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.340908
Coefficient of variation (CV)0.30791018
Kurtosis0.18235534
Mean4.3548673
Median Absolute Deviation (MAD)0.8
Skewness-0.11975812
Sum492.1
Variance1.7980341
MonotonicityNot monotonic
2023-11-10T17:46:12.928248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 7
 
6.2%
4.3 7
 
6.2%
2.9 5
 
4.4%
4.4 5
 
4.4%
4.2 5
 
4.4%
4.1 4
 
3.5%
3.7 4
 
3.5%
4.8 4
 
3.5%
5.5 4
 
3.5%
5 4
 
3.5%
Other values (40) 64
56.6%
ValueCountFrequency (%)
1.3 2
1.8%
1.4 1
0.9%
1.6 1
0.9%
1.7 1
0.9%
1.8 1
0.9%
2 2
1.8%
2.1 1
0.9%
2.3 1
0.9%
2.5 1
0.9%
2.6 1
0.9%
ValueCountFrequency (%)
7.8 1
0.9%
7.7 1
0.9%
7.6 1
0.9%
6.6 1
0.9%
6.5 1
0.9%
6.4 1
0.9%
6.3 2
1.8%
6.2 1
0.9%
6.1 1
0.9%
5.9 1
0.9%

routine_culturing_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.79292
Minimum1.6
Maximum60.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:13.180255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile3.32
Q18.4
median14.1
Q320.3
95-th percentile35.22
Maximum60.5
Range58.9
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation10.234707
Coefficient of variation (CV)0.64805667
Kurtosis3.9665355
Mean15.79292
Median Absolute Deviation (MAD)5.8
Skewness1.6101772
Sum1784.6
Variance104.74924
MonotonicityNot monotonic
2023-11-10T17:46:13.393346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9 3
 
2.7%
11.6 2
 
1.8%
14.6 2
 
1.8%
15.6 2
 
1.8%
3.8 2
 
1.8%
9.9 2
 
1.8%
8.4 2
 
1.8%
12.3 2
 
1.8%
6.2 2
 
1.8%
17.7 2
 
1.8%
Other values (86) 92
81.4%
ValueCountFrequency (%)
1.6 1
0.9%
1.9 1
0.9%
2.2 1
0.9%
2.5 1
0.9%
2.6 2
1.8%
3.8 2
1.8%
4.1 1
0.9%
4.6 1
0.9%
5.2 1
0.9%
5.7 1
0.9%
ValueCountFrequency (%)
60.5 1
0.9%
52.4 1
0.9%
46 1
0.9%
42 1
0.9%
36.7 1
0.9%
36.3 1
0.9%
34.5 1
0.9%
30.2 1
0.9%
29.6 1
0.9%
28.5 1
0.9%

routine_xray_ratio
Real number (ℝ)

Distinct104
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.628319
Minimum39.6
Maximum133.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:13.597627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.6
5-th percentile49.7
Q169.5
median82.3
Q394.1
95-th percentile113.34
Maximum133.5
Range93.9
Interquartile range (IQR)24.6

Descriptive statistics

Standard deviation19.363826
Coefficient of variation (CV)0.23721947
Kurtosis-0.23906704
Mean81.628319
Median Absolute Deviation (MAD)12.8
Skewness0.0078777446
Sum9224
Variance374.95776
MonotonicityNot monotonic
2023-11-10T17:46:13.796258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.2 2
 
1.8%
65.7 2
 
1.8%
88.9 2
 
1.8%
79.8 2
 
1.8%
86.9 2
 
1.8%
79.5 2
 
1.8%
95.9 2
 
1.8%
116.9 2
 
1.8%
87.5 2
 
1.8%
88.4 1
 
0.9%
Other values (94) 94
83.2%
ValueCountFrequency (%)
39.6 1
0.9%
40.4 1
0.9%
42.6 1
0.9%
45.7 1
0.9%
46.5 1
0.9%
47 1
0.9%
51.5 1
0.9%
51.7 1
0.9%
54.9 1
0.9%
55.9 1
0.9%
ValueCountFrequency (%)
133.5 1
0.9%
122.8 1
0.9%
122 1
0.9%
116.9 2
1.8%
113.7 1
0.9%
113.1 1
0.9%
112.6 1
0.9%
111.7 1
0.9%
108.7 1
0.9%
105.3 1
0.9%

num_beds
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.16814
Minimum29
Maximum835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:14.004902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile69.2
Q1106
median186
Q3312
95-th percentile628
Maximum835
Range806
Interquartile range (IQR)206

Descriptive statistics

Standard deviation192.84269
Coefficient of variation (CV)0.7647385
Kurtosis1.2814702
Mean252.16814
Median Absolute Deviation (MAD)94
Skewness1.3786163
Sum28495
Variance37188.302
MonotonicityNot monotonic
2023-11-10T17:46:14.234811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 3
 
2.7%
195 3
 
2.7%
98 2
 
1.8%
167 2
 
1.8%
318 2
 
1.8%
92 2
 
1.8%
95 2
 
1.8%
130 2
 
1.8%
115 2
 
1.8%
72 2
 
1.8%
Other values (88) 91
80.5%
ValueCountFrequency (%)
29 1
0.9%
52 1
0.9%
56 1
0.9%
60 1
0.9%
64 1
0.9%
68 1
0.9%
70 1
0.9%
72 2
1.8%
73 1
0.9%
76 2
1.8%
ValueCountFrequency (%)
835 1
0.9%
833 1
0.9%
831 1
0.9%
768 1
0.9%
752 1
0.9%
640 1
0.9%
620 1
0.9%
600 1
0.9%
595 1
0.9%
593 1
0.9%

med_school_affil
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2
96 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

Length

2023-11-10T17:46:14.449112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T17:46:14.589841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

Most occurring characters

ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 96
85.0%
1 17
 
15.0%

region
Categorical

Distinct4
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3
37 
2
32 
1
28 
4
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

Length

2023-11-10T17:46:14.749129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T17:46:14.935304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

Most occurring characters

ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 113
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 37
32.7%
2 32
28.3%
1 28
24.8%
4 16
14.2%

avg_census
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.37168
Minimum20
Maximum791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:15.189171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q168
median143
Q3252
95-th percentile499.2
Maximum791
Range771
Interquartile range (IQR)184

Descriptive statistics

Standard deviation153.75956
Coefficient of variation (CV)0.80346038
Kurtosis1.730374
Mean191.37168
Median Absolute Deviation (MAD)84
Skewness1.3793894
Sum21625
Variance23642.003
MonotonicityNot monotonic
2023-11-10T17:46:15.457848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 3
 
2.7%
59 3
 
2.7%
207 2
 
1.8%
37 2
 
1.8%
51 2
 
1.8%
65 2
 
1.8%
90 2
 
1.8%
69 2
 
1.8%
217 2
 
1.8%
127 2
 
1.8%
Other values (86) 91
80.5%
ValueCountFrequency (%)
20 1
0.9%
37 2
1.8%
38 1
0.9%
39 1
0.9%
40 2
1.8%
42 1
0.9%
44 1
0.9%
47 2
1.8%
49 1
0.9%
50 1
0.9%
ValueCountFrequency (%)
791 1
0.9%
595 1
0.9%
591 1
0.9%
581 1
0.9%
547 1
0.9%
546 1
0.9%
468 1
0.9%
452 1
0.9%
446 1
0.9%
441 1
0.9%

num_nurses
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.24779
Minimum14
Maximum656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:15.669217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile35
Q166
median132
Q3218
95-th percentile455.2
Maximum656
Range642
Interquartile range (IQR)152

Descriptive statistics

Standard deviation139.26539
Coefficient of variation (CV)0.8038509
Kurtosis1.5535566
Mean173.24779
Median Absolute Deviation (MAD)70
Skewness1.378771
Sum19577
Variance19394.849
MonotonicityNot monotonic
2023-11-10T17:46:15.864556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 3
 
2.7%
55 2
 
1.8%
335 2
 
1.8%
172 2
 
1.8%
182 2
 
1.8%
56 2
 
1.8%
49 2
 
1.8%
73 2
 
1.8%
112 2
 
1.8%
79 2
 
1.8%
Other values (87) 92
81.4%
ValueCountFrequency (%)
14 1
 
0.9%
19 1
 
0.9%
21 1
 
0.9%
22 1
 
0.9%
32 1
 
0.9%
35 3
2.7%
38 1
 
0.9%
42 1
 
0.9%
44 1
 
0.9%
45 1
 
0.9%
ValueCountFrequency (%)
656 1
0.9%
629 1
0.9%
528 1
0.9%
519 1
0.9%
497 1
0.9%
469 1
0.9%
446 1
0.9%
436 1
0.9%
420 1
0.9%
407 1
0.9%

avelbl_services
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.159292
Minimum5.7
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:16.085517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.7
5-th percentile21.74
Q131.4
median42.9
Q354.3
95-th percentile68.6
Maximum80
Range74.3
Interquartile range (IQR)22.9

Descriptive statistics

Standard deviation15.200861
Coefficient of variation (CV)0.35220368
Kurtosis-0.41828308
Mean43.159292
Median Absolute Deviation (MAD)11.4
Skewness0.074180828
Sum4877
Variance231.06619
MonotonicityNot monotonic
2023-11-10T17:46:16.295715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
45.7 10
 
8.8%
37.1 9
 
8.0%
48.6 8
 
7.1%
51.4 8
 
7.1%
40 8
 
7.1%
22.9 7
 
6.2%
57.1 7
 
6.2%
34.3 7
 
6.2%
28.6 7
 
6.2%
54.3 6
 
5.3%
Other values (16) 36
31.9%
ValueCountFrequency (%)
5.7 1
 
0.9%
11.4 1
 
0.9%
14.3 1
 
0.9%
17.1 2
 
1.8%
20 1
 
0.9%
22.9 7
6.2%
25.7 5
4.4%
28.6 7
6.2%
31.4 5
4.4%
34.3 7
6.2%
ValueCountFrequency (%)
80 1
 
0.9%
77.1 1
 
0.9%
74.3 1
 
0.9%
71.4 1
 
0.9%
68.6 3
2.7%
65.7 3
2.7%
62.9 5
4.4%
60 2
 
1.8%
57.1 7
6.2%
54.3 6
5.3%

ln_num_beds
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2663684
Minimum3.3672958
Maximum6.7274317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:16.500770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3672958
5-th percentile4.2369002
Q14.6634391
median5.2257467
Q35.7430032
95-th percentile6.442419
Maximum6.7274317
Range3.3601359
Interquartile range (IQR)1.0795641

Descriptive statistics

Standard deviation0.73176481
Coefficient of variation (CV)0.13895055
Kurtosis-0.64883813
Mean5.2663684
Median Absolute Deviation (MAD)0.54361545
Skewness0.11396846
Sum595.09963
Variance0.53547973
MonotonicityNot monotonic
2023-11-10T17:46:16.716310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.697093487 3
 
2.7%
5.272999559 3
 
2.7%
4.584967479 2
 
1.8%
5.117993812 2
 
1.8%
5.762051383 2
 
1.8%
4.521788577 2
 
1.8%
4.553876892 2
 
1.8%
4.86753445 2
 
1.8%
4.744932128 2
 
1.8%
4.276666119 2
 
1.8%
Other values (88) 91
80.5%
ValueCountFrequency (%)
3.36729583 1
0.9%
3.951243719 1
0.9%
4.025351691 1
0.9%
4.094344562 1
0.9%
4.158883083 1
0.9%
4.219507705 1
0.9%
4.248495242 1
0.9%
4.276666119 2
1.8%
4.290459441 1
0.9%
4.33073334 2
1.8%
ValueCountFrequency (%)
6.727431725 1
0.9%
6.725033642 1
0.9%
6.722629795 1
0.9%
6.643789733 1
0.9%
6.622736324 1
0.9%
6.461468176 1
0.9%
6.429719478 1
0.9%
6.396929655 1
0.9%
6.388561406 1
0.9%
6.385194399 1
0.9%

ln_avg_census
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9442343
Minimum2.9957323
Maximum6.673298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:16.940699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9957323
5-th percentile3.6888795
Q14.2195077
median4.9628446
Q35.5294291
95-th percentile6.2101286
Maximum6.673298
Range3.6775657
Interquartile range (IQR)1.3099214

Descriptive statistics

Standard deviation0.80927801
Coefficient of variation (CV)0.16368116
Kurtosis-0.86390103
Mean4.9442343
Median Absolute Deviation (MAD)0.63557733
Skewness-0.0053030407
Sum558.69848
Variance0.6549309
MonotonicityNot monotonic
2023-11-10T17:46:17.249185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.727387819 3
 
2.7%
4.077537444 3
 
2.7%
5.332718793 2
 
1.8%
3.610917913 2
 
1.8%
3.931825633 2
 
1.8%
4.17438727 2
 
1.8%
4.49980967 2
 
1.8%
4.234106505 2
 
1.8%
5.379897354 2
 
1.8%
4.844187086 2
 
1.8%
Other values (86) 91
80.5%
ValueCountFrequency (%)
2.995732274 1
0.9%
3.610917913 2
1.8%
3.63758616 1
0.9%
3.663561646 1
0.9%
3.688879454 2
1.8%
3.737669618 1
0.9%
3.784189634 1
0.9%
3.850147602 2
1.8%
3.891820298 1
0.9%
3.912023005 1
0.9%
ValueCountFrequency (%)
6.673297968 1
0.9%
6.388561406 1
0.9%
6.381816017 1
0.9%
6.364750757 1
0.9%
6.304448802 1
0.9%
6.302618976 1
0.9%
6.148468296 1
0.9%
6.11368218 1
0.9%
6.100318952 1
0.9%
6.089044875 1
0.9%

ln_num_nurses
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8372443
Minimum2.6390573
Maximum6.4861608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-11-10T17:46:17.490688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6390573
5-th percentile3.5553481
Q14.1896547
median4.8828019
Q35.3844951
95-th percentile6.1204325
Maximum6.4861608
Range3.8471035
Interquartile range (IQR)1.1948403

Descriptive statistics

Standard deviation0.83344974
Coefficient of variation (CV)0.17229846
Kurtosis-0.46216052
Mean4.8372443
Median Absolute Deviation (MAD)0.59366163
Skewness-0.19817133
Sum546.60861
Variance0.69463847
MonotonicityNot monotonic
2023-11-10T17:46:17.703895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.555348061 3
 
2.7%
4.007333185 2
 
1.8%
5.814130532 2
 
1.8%
5.147494477 2
 
1.8%
5.204006687 2
 
1.8%
4.025351691 2
 
1.8%
3.891820298 2
 
1.8%
4.290459441 2
 
1.8%
4.718498871 2
 
1.8%
4.369447852 2
 
1.8%
Other values (87) 92
81.4%
ValueCountFrequency (%)
2.63905733 1
 
0.9%
2.944438979 1
 
0.9%
3.044522438 1
 
0.9%
3.091042453 1
 
0.9%
3.465735903 1
 
0.9%
3.555348061 3
2.7%
3.63758616 1
 
0.9%
3.737669618 1
 
0.9%
3.784189634 1
 
0.9%
3.80666249 1
 
0.9%
ValueCountFrequency (%)
6.486160789 1
0.9%
6.444131257 1
0.9%
6.269096284 1
0.9%
6.251903883 1
0.9%
6.208590026 1
0.9%
6.150602768 1
0.9%
6.100318952 1
0.9%
6.077642243 1
0.9%
6.040254711 1
0.9%
6.008813185 1
0.9%

Interactions

2023-11-10T17:46:08.943848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:47.230351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.701842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.540526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.470974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.436109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.212688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.055419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.255734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.036886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.863415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:06.835941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.152102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:47.485012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.829813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.672448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.615623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.569661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.366520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.214134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.401958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.196878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.047686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.037719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.327599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:47.688245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.000700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.794953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.747591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.693403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.504850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.356272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.531936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.338653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.194165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.233452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.493518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:48.235430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.151459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.941618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.912908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.829128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.646556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.511793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.667009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.485763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.360167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.459970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.629747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:48.409379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.298877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.086286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.069360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.967739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.781917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.684850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.795841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.620314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.510513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.596722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.747514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:48.568055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.428290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.223358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.213769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.102438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.917447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.813377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.928090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.743475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.663469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.721754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:09.885374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:48.761469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.600613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.382036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.371648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.249832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.080205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:59.981168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.085269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:03.889131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.807006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:07.868685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:10.034815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:48.952765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.770616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.535190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.563127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.405735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.233479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:00.423168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.240127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.042811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:05.988822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:08.059440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:10.169583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.108914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:50.937676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.691942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.735636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.549132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.376727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:00.562061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.378528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.190995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:06.175536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:08.220049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:10.332652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.265703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.092193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:52.878021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:54.916948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.697241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.529885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:00.716671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.525913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.344918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:06.340233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:08.389217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:10.501873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.410013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.236265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.072607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.083194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:56.866844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.677087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:00.904120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.667634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.510705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:06.479598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:08.547380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:10.670286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:49.556333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:51.393354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:53.275368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:55.266380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:57.052163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:45:58.861768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:01.083653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:02.847226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:04.671403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:06.652289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-10T17:46:08.710144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-10T17:46:17.866424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lenght_of_stayageinfection_riskroutine_culturing_ratioroutine_xray_rationum_bedsavg_censusnum_nursesavelbl_servicesln_num_bedsln_avg_censusln_num_nursesmed_school_affilregion
lenght_of_stay1.0000.1130.5490.3460.3200.5030.5300.4710.3900.5030.5300.4710.3150.268
age0.1131.000-0.021-0.171-0.077-0.135-0.146-0.150-0.042-0.135-0.146-0.1500.0000.000
infection_risk0.549-0.0211.0000.5600.3750.4430.4470.4880.3900.4430.4470.4880.0290.000
routine_culturing_ratio0.346-0.1710.5601.0000.4800.2040.2090.2820.2140.2040.2090.2820.2400.194
routine_xray_ratio0.320-0.0770.3750.4801.0000.0710.0680.1090.0650.0710.0680.1090.2360.067
num_beds0.503-0.1350.4430.2040.0711.0000.9810.9350.8541.0000.9810.9350.5420.134
avg_census0.530-0.1460.4470.2090.0680.9811.0000.9310.8370.9811.0000.9310.5770.178
num_nurses0.471-0.1500.4880.2820.1090.9350.9311.0000.8560.9350.9311.0000.6700.131
avelbl_services0.390-0.0420.3900.2140.0650.8540.8370.8561.0000.8540.8370.8560.5250.113
ln_num_beds0.503-0.1350.4430.2040.0711.0000.9810.9350.8541.0000.9810.9350.4960.000
ln_avg_census0.530-0.1460.4470.2090.0680.9811.0000.9310.8370.9811.0000.9310.5560.155
ln_num_nurses0.471-0.1500.4880.2820.1090.9350.9311.0000.8560.9350.9311.0000.5500.149
med_school_affil0.3150.0000.0290.2400.2360.5420.5770.6700.5250.4960.5560.5501.0000.000
region0.2680.0000.0000.1940.0670.1340.1780.1310.1130.0000.1550.1490.0001.000

Missing values

2023-11-10T17:46:10.906820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-10T17:46:11.264958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

lenght_of_stayageinfection_riskroutine_culturing_ratioroutine_xray_rationum_bedsmed_school_affilregionavg_censusnum_nursesavelbl_servicesln_num_bedsln_avg_censusln_num_nurses
17.1355.74.19.039.62792420724160.05.6312125.3327195.484797
28.8258.21.63.851.78022515240.04.3820273.9318263.951244
38.3456.92.78.174.010723825420.04.6728294.4067193.988984
48.9553.75.618.9122.8147245314840.04.9904333.9702924.997212
511.2056.55.734.588.91802113415140.05.1929574.8978405.017280
69.7650.95.121.997.01502214710640.05.0106354.9904334.663439
79.6857.84.616.779.01862315112940.05.2257475.0172804.859812
811.1845.75.460.585.86401239936060.06.4614685.9889615.886104
98.6748.24.324.490.81822313011840.05.2040074.8675344.770685
108.8456.36.329.682.68521596640.04.4426514.0775374.189655
lenght_of_stayageinfection_riskroutine_culturing_ratioroutine_xray_rationum_bedsmed_school_affilregionavg_censusnum_nursesavelbl_servicesln_num_bedsln_avg_censusln_num_nurses
10413.9565.96.615.6133.53562130818262.95.8749315.7301005.204007
1059.4452.54.510.958.52972323026342.95.6937325.4380795.572154
10610.8063.92.91.657.413023696222.94.8675344.2341074.127134
1077.1451.71.44.145.711523901922.94.7449324.4998102.944439
1088.0255.02.13.846.59122443222.94.5108603.7841903.465736
10911.8053.85.79.1116.95711244146962.96.3473896.0890456.150603
1109.5049.35.842.070.99823684622.94.5849674.2195083.828641
1117.7056.94.412.267.9129248513662.94.8598124.4426514.912655
11217.9456.25.926.491.88351179140762.96.7274326.6732986.008813
1139.4159.53.120.691.72923202222.93.3672962.9957323.091042